Allocation of storage resources in a networked computing environment based on energy utilization

ABSTRACT

The present invention provides an approach to provision storage resources (e.g., across an enterprise storage system) for different workloads in an energy efficient manner. Typically, energy consumption characteristics for handling a particular storage workload will be determined. Thereafter, a type of storage device capable of handling the workload will be determined. Then, an allocation plan that results in the most efficient energy consumption for handling the workload will be developed. The allocation plan is based upon the energy consumption characteristics and an energy efficiency algorithm. The energy efficiency algorithm serves to identify storage device(s) that can handle the workload in such a way as to reduce total energy consumption and, accordingly, costs. The energy efficiency algorithm may also consider other factors such as capacity and load of storage devices and service level agreement (SLA) terms. At least one storage device can then be selected for handling the storage workload.

RELATED U.S. APPLICATION DATA

The present patent document is a continuation of U.S. patent applicationSer. No. 13/073,081, filed Mar. 28, 2011, entitled “ALLOCATION OFSTORAGE RESOURCES IN A NETWORKED COMPUTING ENVIRONMENT BASED ON ENERGYUTILIZATION”. The disclosure of U.S. patent application Ser. No.13/073,081 is incorporated herein by reference.

TECHNICAL FIELD

The present invention generally relates to allocation of storageresources based on energy utilization. Specifically, the presentinvention relates to the provision of storage resources for differentworkloads in an energy efficient manner in a networked computingenvironment (e.g., in a cloud computing environment).

BACKGROUND

The cloud computing environment is an enhancement to the predecessorgrid environment, whereby multiple grids and other computation resourcesmay be further abstracted by a cloud layer, thus making disparatedevices appear to an end-consumer as a single pool of seamlessresources. These resources may include such things as physical orlogical computing engines, servers and devices, device memory, andstorage devices.

Energy consumption is becoming a growing concern for enterprise storageclouds. Specifically, as workloads are added to a storage cloud, thecorresponding power/energy consumption goes up, which can drive upoperational costs. Different workloads have different characteristicsthat may be defined in terms of Input/Output (I/O) per second, cache hitrate, read-write ratio, random-sequential ratio, etc. In addition to thetype and configuration of underlying storage resources, these workloadparameters also influence the amount of energy consumed by thecorresponding workloads. Ad-hoc allocation of storage resources mayresult in inefficient resource utilization as well as higher energyconsumption.

SUMMARY

Embodiments of the present invention provide an approach to provisionstorage resources (e.g., across an enterprise storage system (ESS) suchas a general parallel file system (GPFS) or the like) for differentworkloads in an energy efficient manner. The system evaluates differentenergy profiles/workloads' energy consumption characteristics of storagedevices to determine an allocation plan that reduces the energy cost(e.g., results in the lowest cost/energy consumption for handling astorage workload). In a typical embodiment, energy consumptioncharacteristics for handling a particular storage workload will bedetermined. Thereafter, a type of storage device capable of handling theworkload will be determined. Then, an allocation plan that results inthe most efficient energy consumption for handling the workload will bedeveloped. In general, the allocation plan is based upon the energyconsumption characteristics and an energy efficiency algorithm. Theenergy efficiency algorithm serves to identify storage device(s) thatcan handle the workload in such a way as to reduce total energyconsumption and, accordingly, costs. Along these lines, the energyefficiency algorithm may also consider other factors such as capacityand load of storage devices and service level agreement (SLA) terms inaddition to energy costs (e.g., over times of day and/or days of week).In any event, at least one storage device can then be selected forhandling the storage workload according to the allocation plan.

A first aspect of the present invention provides a method for energyefficient allocation of storage resources in a networked computingenvironment, comprising: determining energy consumption characteristicsof a storage workload in the networked computing environment; selectinga type of storage device for handling the storage workload; developingan allocation plan to result in a most efficient energy consumption forhandling the workload, the allocation plan being based upon the energyconsumption characteristics, a set of device models for a set of storagedevices having the type, and an energy efficiency algorithm; andselecting at least one storage device from the set of storage devicesfor handling the storage workload according to the allocation plan.

A second aspect of the present invention provides a system for energyefficient allocation of storage resources in a networked computingenvironment, comprising: a bus; a processor coupled to the bus; and amemory medium coupled to the bus, the memory medium comprisinginstructions to: determine energy consumption characteristics of astorage workload in the networked computing environment; select a typeof storage device for handling the storage workload; develop anallocation plan to result in a most efficient energy consumption forhandling the workload, the allocation plan being based upon the energyconsumption characteristics, a set of device models for a set of storagedevices having the type, and an energy efficiency algorithm; and selectat least one storage device from the set of storage devices for handlingthe storage workload according to the allocation plan.

A third aspect of the present invention provides a computer programproduct for energy efficient allocation of storage resources in anetworked computing environment, the computer program productcomprising: a computer readable storage media, and program instructionsstored on the computer readable storage media, to: determine energyconsumption characteristics of a storage workload in the networkedcomputing environment; select a type of storage device for handling thestorage workload; develop an allocation plan to result in a mostefficient energy consumption for handling the workload, the allocationplan being based upon the energy consumption characteristics, a set ofdevice models for a set of storage devices having the type, and anenergy efficiency algorithm; and select at least one storage device fromthe set of storage devices for handling the storage workload accordingto the allocation plan.

A fourth aspect of the present invention provides a method for deployinga system for energy efficient allocation of storage resources in anetworked computing environment, comprising: deploying a computerinfrastructure being operable to: determine energy consumptioncharacteristics of a storage workload in the networked computingenvironment; select a type of storage device for handling the storageworkload; develop an allocation plan to result in a most efficientenergy consumption for handling the workload, the allocation plan beingbased upon the energy consumption characteristics, a set of devicemodels for a set of storage devices having the type, and an energyefficiency algorithm; and select at least one storage device from theset of storage devices for handling the storage workload according tothe allocation plan.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features of this invention will be more readilyunderstood from the following detailed description of the variousaspects of the invention taken in conjunction with the accompanyingdrawings in which:

FIG. 1 depicts a cloud computing node according to an embodiment of thepresent invention.

FIG. 2 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 3 depicts abstraction model layers according to an embodiment ofthe present invention.

FIG. 4 depicts a process flow diagram according to an embodiment of thepresent invention.

FIG. 5 depicts an enterprise storage system (ESS) according to anembodiment of the present invention.

FIG. 6 depicts a method flow diagram according to an embodiment of thepresent invention.

The drawings are not necessarily to scale. The drawings are merelyschematic representations, not intended to portray specific parametersof the invention. The drawings are intended to depict only typicalembodiments of the invention, and therefore should not be considered aslimiting the scope of the invention. In the drawings, like numberingrepresents like elements.

DETAILED DESCRIPTION

Illustrative embodiments now will be described more fully herein withreference to the accompanying drawings, in which exemplary embodimentsare shown. This disclosure may, however, be embodied in many differentforms and should not be construed as limited to the exemplaryembodiments set forth herein. Rather, these exemplary embodiments areprovided so that this disclosure will be thorough and complete and willfully convey the scope of this disclosure to those skilled in the art.In the description, details of well-known features and techniques may beomitted to avoid unnecessarily obscuring the presented embodiments.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of this disclosure.As used herein, the singular forms “a”, “an”, and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. Furthermore, the use of the terms “a”, “an”, etc., do notdenote a limitation of quantity, but rather denote the presence of atleast one of the referenced items. It will be further understood thatthe terms “comprises” and/or “comprising”, or “includes” and/or“including”, when used in this specification, specify the presence ofstated features, regions, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, regions, integers, steps, operations, elements,components, and/or groups thereof.

Enterprise storage clouds are typically comprised of a large number ofinterconnected components such as servers, switches, raid arrays, disks,etc. Over a period of time, some of these components may become aperformance bottleneck. This may be due to change in workload, systemmis-configurations, component failure, etc. A component causing energyinefficiency may deteriorate cloud performance, reduce availability, orresult in service level agreement (SLA) violation. These may furtherlead to loss in revenue, customer dissatisfaction, etc.

Embodiments of the present invention provide an approach to provisionstorage resources (e.g., across an enterprise storage system (ESS) suchas a general parallel file system (GPFS) or the like) for differentworkloads in an energy efficient manner. The system evaluates differentenergy profiles/workloads' energy consumption characteristics of storagedevices to determine an allocation plan that reduces the energy cost(e.g., results in the lowest cost/energy consumption for handling astorage workload). In a typical embodiment, energy consumptioncharacteristics for handling a particular storage workload will bedetermined. Thereafter, a type of storage device capable of handling theworkload will be determined. Then, an allocation plan that results inthe most efficient energy consumption for handling the workload will bedeveloped. In general, the allocation plan is based upon the energyconsumption characteristics and an energy efficiency algorithm. Theenergy efficiency algorithm serves to identify storage device(s) thatcan handle the workload in such a way as to reduce total energyconsumption and, accordingly, costs. Along these lines, the energyefficiency algorithm may also consider other factors such as capacityand load of storage devices and service level agreement (SLA) terms inaddition to energy costs (e.g., over times of day and/or days of week).In any event, at least one storage device can then be selected forhandling the storage workload according to the allocation plan.

It is further understood in advance that although this disclosureincludes a detailed description of cloud computing, implementation ofthe teachings recited herein are not limited to a cloud computingenvironment. Rather, embodiments of the present invention are capable ofbeing implemented in conjunction with any other type of computingenvironment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded, automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active consumer accounts). Resource usage canbe monitored, controlled, and reported providing transparency for boththe provider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited consumer-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication-hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service-oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10, there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM, or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

The embodiments of the invention may be implemented as a computerreadable signal medium, which may include a propagated data signal withcomputer readable program code embodied therein (e.g., in baseband or aspart of a carrier wave). Such a propagated signal may take any of avariety of forms including, but not limited to, electro-magnetic,optical, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that can communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium including, but not limited to, wireless,wireline, optical fiber cable, radio-frequency (RF), etc., or anysuitable combination of the foregoing.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a consumer to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via I/O interfaces22. Still yet, computer system/server 12 can communicate with one ormore networks such as a local area network (LAN), a general wide areanetwork (WAN), and/or a public network (e.g., the Internet) via networkadapter 20. As depicted, network adapter 20 communicates with the othercomponents of computer system/server 12 via bus 18. It should beunderstood that although not shown, other hardware and/or softwarecomponents could be used in conjunction with computer system/server 12.Examples include, but are not limited to: microcode, device drivers,redundant processing units, external disk drive arrays, RAID systems,tape drives, and data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as private, community,public, or hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms, and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 2 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only, and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include mainframes. In oneexample, IBM® zSeries® systems and RISC (Reduced Instruction SetComputer) architecture based servers. In one example, IBM pSeries®systems, IBM xSeries® systems, IBM BladeCenter® systems, storagedevices, networks, and networking components. Examples of softwarecomponents include network application server software. In one example,IBM WebSphere® application server software and database software. In oneexample, IBM DB2® database software. (IBM, zSeries, pSeries, xSeries,BladeCenter, WebSphere, and DB2 are trademarks of International BusinessMachines Corporation registered in many jurisdictions worldwide.)

Virtualization layer 62 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer 64 may provide the functions describedbelow. Resource provisioning provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and pricing provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.Consumer portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provides pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA.

Workloads layer 66 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; and storage resource allocation. As mentioned above, all ofthe foregoing examples described with respect to FIG. 3 are illustrativeonly, and the invention is not limited to these examples.

It is understood all that functions of the present invention asdescribed herein typically may be performed by the storage resourceallocation functionality, which can be tangibly embodied as modules ofprogram code 42 of program/utility 40 (FIG. 1). However, this need notbe the case. Rather, the functionality recited herein could be carriedout/implemented and/or enabled by any of the layers 60-66 shown in FIG.3.

It is reiterated that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather, theembodiments of the present invention are intended to be implemented withany type of networked computing environment now known or laterdeveloped.

In general, enterprise datacenters are facing serious challenges to copewith the exponential growth of storage and computing needs. Thesechallenges include cost, performance, and resiliency, among otherthings. Architectures such as cloud computing environments are beingproposed, which address some of the challenges (e.g., managementissues). However, some of the issues still remain. One of thosechallenges is the increase in energy consumption requirements. Systemadministrators not only have to manage performance, resiliency, andother workload requirements, but also the needs of energy-consumingservers, storage, and networking equipments. As more workloads areconsolidated in large cloud computing environments, the need foradditional energy is also growing. These consolidated environmentsshould be designed to accommodate peak energy and cooling requirements.Failure to do so may result in system failure and/or applicationdowntime, both of which may cause data and revenue loss, missedopportunities, and loss of goodwill. Energy consumed by a particularapplication and workload depends not only on their I/O and computationalcharacteristics, but also on the type and configuration of the resourcesthey use. Arbitrary allocation and configuration of cloud resources mayresult in higher energy consumption and may exceed the total energybudget allocated for a particular cloud installation.

Storage cloud services can be built on top of shared networkedinfrastructure(s) with the ability to scale to vast quantities ofstorage and an astronomical number of I/O transactions per second withlow cost and high reliability. These storage services are typically madeavailable in the form of web services API(s) or traditional filesystem-based interfaces. As indicated, embodiments of the presentinvention provide an approach for allocating storage (e.g., handling astorage workload) in an enterprise storage system (ESS) such as ageneral parallel file system (GPFS). It is understood that although aGPFS is used herein as an example, the same concepts could be applied toany type of ESS.

In general, a GPFS is based on a shared disk architecture, where thefile system data is striped across multiple storage units called networkshared disks (NSDs). These NSDs are typically created out of storagelogical units (LUNs) that may physically reside in one or more storagesubsystem(s). Storage LUNs are typically created out of redundant arraysof independent disks (e.g., a RAID-based storage pool having multiplestorage disks). Data stored in a LUN is potentially spread acrossseveral disks in the storage pool. This sharing and distribution can bebeneficial for performance and fault tolerance, but it can have anegative impact on the energy consumption of the workloads. This isbecause the distribution of a file system workload across multipleNSDs/LUNs causes all the disks that constitute those LUNs to remain inan active state (e.g., “spinning”). Also, different types of disks(e.g., serial-attached small computer system interface (SAS), serialadvanced technology attachment (SATA), solid state drives (SSDs), etc.)consume different amounts of energy and have different performance andreliability characteristics. The approach recited herein providesautomated selection of NSDs/LUNs for a given file system workload whileoptimizing energy and other workload requirements.

Referring now to FIG. 4, a process flow diagram according to the anembodiment present invention is shown. As depicted, in steps P1 and P2,a storage configuration is determined/discovered. Specifically, storageresources/components/devices of an ESS (e.g., GPFS 70 of FIG. 5), theirconfigurations and interrelationships are determined. Briefly referringto FIG. 5, such resources/components/devices can include RAID pools80A-N that can be coupled to LUNs 78A-N. As further shown, LUNs 78A-Ncan be coupled to NSDs 76A-N of clusters 74A-N (e.g., GPFS clusters). Inany event, referring back to FIG. 4, the configuration information isfed to workload optimizer (e.g., which can be embodied asprogram/utility 40 of FIG. 1, and which enables storage resourceallocation functionality in workloads layer 66 of FIG. 3) in step P5.Also received by workload optimizer P5 are workloadcharacteristics/information (e.g., energy consumption characteristicsinvolved with handling/servicing a given storage workload) in step P3,and device models in step P4. The device models indicate an energyconsumption rate/information about the resources/components/devices inthe discovered ESS. Such information can also include a current load oneach component/resource, a capacity of each component/resource, and arate at which each resource/component/device consumes energy under agiven load and/or available capacity.

Based on the received information (e.g., storage configuration, workloadcharacteristics, device models, etc.), the workload optimizer will applyan energy efficiency algorithm to develop an allocation plan P6 forhandling the storage workload. Along these lines, the allocation planincludes instructions for allocating the storage workload so that thelevel of energy needed to handle the storage workload can be provided soas to result in the most efficient energy consumption/cost. For example,the allocation plan can call for using a set (at least one) of storagedevices of a certain type in a certain manner (e.g., in a certain order,to a certain load, etc.). The energy efficiency algorithm is typicallybased on the capacity of a set of storage devices, a load on each of theset of storage devices, and an energy consumption of the set of storagedevices as derived from the set of device models. As such the workloadoptimizer could, in a typical embodiment, determine what type of storagedevice(s) should be used to handle the workload, and then determine whatspecific instances of devices having those type(s) should be used toaccommodate the workload. The allocation plan would further be generatedto utilize these devices in the most energy efficient manner. As such,the workload optimizer will balance multiple factors such as handlingthe storage workload and doing so in a way that results in the mostefficient energy consumption/cost (e.g., while still honoring any otherfactors such as service level agreement (SLA) terms or the like). In anyevent, in step P7, the allocation plan will be deployed and the storageworkload handled accordingly.

As indicated above, the workload optimizer can select a type of storagedevice to handle a given storage workload. The selection of the devicetype can be primarily governed by workload performance and resiliencyrequirements. For “mission critical” data with high performance andreliability requirements, SAS is the preferred disk type, SSD isrecommended for workloads with high random I/O, SATA is preferred forredundant data like logs, etc. The system of the embodiments of thepresent invention uses these policies and best practices to choosebetween different disk types.

Once the device type is selected, the energy efficiency algorithm willbe used to select the actual NSDs for file system creation. Since moredisk activities result in higher energy consumption, the algorithm canselect NSDs such that total activity (or entropy) of the underlyingdisks that form the NSDs is minimized. Furthermore, the algorithm canminimize server-level energy consumption by consolidating workloads onfewer storage nodes.

In general, the energy efficiency algorithm can be developed as follows:

-   Let l_(Ni) ^(z) denotes the existing load on NSD (or LUN) N_(i).-   Let l_(Dj) ^(s) denotes the existing load on physical disk D_(i).-   Let C_(D) ^(r) be the total number of existing active disks-   Therefore, total existing load on the disks:

$L_{D}^{s} = {\sum\limits_{j}^{{List}\mspace{14mu}{of}\mspace{14mu}{disks}}\; l_{Dj}^{s}}$

-   Similarly, for provisioning the new workload ‘n’,-   Let l_(Ni) ^(n) denotes the new load on NSD (or LUN) N_(i).-   Let denotes the new load on physical disk-   Let C_(D) ^(n) be the total number of active disks after adding the    new workload-   Therefore, total load on the disks after adding the new workload is:

$L_{D} = {\sum\limits_{j}^{{Lists}\mspace{14mu}{of}\mspace{14mu}{disks}}\;( {l_{Dj}^{s} + l_{Dj}^{n}} )}$

-   For minimizing energy consumption, the optimization algorithm    selects NSDs and storage servers such that the following constraints    are satisfied:-   l_(Dj) ^(z)+l_(dj) ^(n)<C_(nj(i)), where C_(nj) is the performance    capacity of disk D_(i).-   (ii) Minimize L_(D), i.e. Minimize

$\sum\limits_{j}^{{Lists}\mspace{14mu}{of}\mspace{14mu}{Disks}}\;( {l_{Dj}^{s} + l_{Dj}^{n}} )$

-   (iii) Minimize C_(D) ^(n)-   (iv) Minimize active storage server set.

An example of the application of the teachings recited herein is shownin FIG. 5. As indicated above, FIG. 5 depicts a GPFS 70 comprisingclusters 76A-N of NSDs 76A-N, LUNs 78A-N, and RAID pools 80A-N. INgeneral, clusters 76A-N can receive workloads (e.g., storage workloads)from one or more clients 72A-N. Cluster 76B shows when a storageworkload is subject to random allocation among devices. As shown, twodifferent RAID pools 94A-B are utilized, each of which are coupled to aseparate LUN 92A-B. As such, two non-adjacent NSDs 90A-B were selected.In contrast, cluster 76N shows selection in accordance with theembodiments of the present invention (e.g., via the energy efficiencyalgorithm). As shown, one RAID pool 94C is coupled to adjacent LUNS92C-D, which results in usage of two adjacent NSDs 90C-D.

From FIG. 5, it can be seen that if a file system uses two NSDs 90A-B(or LUNs 92A-B) from two different RAID pools 94A-B, it createsactivities in more disks. Selecting LUNs 92C-D from the same RAID pools94C makes the other RAID pool idle and allows the storage system toswitch the latter to low energy state. In a scenario where the firstRAID pool does not have sufficient performance capacity available, thena LUN from another RAID pool can be chosen.

Referring now to FIG. 6, a method flow diagram according to anembodiment of the present invention is shown. As depicted, in step S1,energy consumption characteristics of a storage workload are determined.In step S2, a type of storage device for handling the storage workloadis selected. In step S3, an allocation plan is developed to result inthe most efficient energy consumption for handling the workload. Asmentioned above, the allocation plan is typically based upon the energyconsumption characteristics, a set of device models for a set of storagedevices having the particular type selected, and an energy efficiencyalgorithm. In step S4, at least one storage device is selected from theset of storage devices for handling the storage workload according tothe allocation plan.

While shown and described herein as a storage resource allocationsolution, it is understood that the invention further provides variousalternative embodiments. For example, in one embodiment, the inventionprovides a computer-readable/useable medium that includes computerprogram code to enable a computer infrastructure to provide storageresource allocation functionality as discussed herein. To this extent,the computer-readable/useable medium includes program code thatimplements each of the various processes of the invention. It isunderstood that the terms computer-readable medium or computer-useablemedium comprise one or more of any type of physical embodiment of theprogram code. In particular, the computer-readable/useable medium cancomprise program code embodied on one or more portable storage articlesof manufacture (e.g., a compact disc, a magnetic disk, a tape, etc.), onone or more data storage portions of a computing device, such as memory28 (FIG. 1) and/or storage system 34 (FIG. 1) (e.g., a fixed disk, aread-only memory, a random access memory, a cache memory, etc.).

In another embodiment, the invention provides a method that performs theprocess of the invention on a subscription, advertising, and/or feebasis. That is, a service provider, such as a Solution Integrator, couldoffer to provide storage resource allocation functionality. In thiscase, the service provider can create, maintain, support, etc., acomputer infrastructure, such as computer system 12 (FIG. 1) thatperforms the processes of the invention for one or more consumers. Inreturn, the service provider can receive payment from the consumer(s)under a subscription and/or fee agreement and/or the service providercan receive payment from the sale of advertising content to one or morethird parties.

In still another embodiment, the invention provides acomputer-implemented method for storage resource allocation. In thiscase, a computer infrastructure, such as computer system 12 (FIG. 1),can be provided and one or more systems for performing the processes ofthe invention can be obtained (e.g., created, purchased, used, modified,etc.) and deployed to the computer infrastructure. To this extent, thedeployment of a system can comprise one or more of: (1) installingprogram code on a computing device, such as computer system 12 (FIG. 1),from a computer-readable medium; (2) adding one or more computingdevices to the computer infrastructure; and (3) incorporating and/ormodifying one or more existing systems of the computer infrastructure toenable the computer infrastructure to perform the processes of theinvention.

As used herein, it is understood that the terms “program code” and“computer program code” are synonymous and mean any expression, in anylanguage, code, or notation, of a set of instructions intended to causea computing device having an information processing capability toperform a particular function either directly or after either or both ofthe following: (a) conversion to another language, code, or notation;and/or (b) reproduction in a different material form. To this extent,program code can be embodied as one or more of: an application/softwareprogram, component software/a library of functions, an operating system,a basic device system/driver for a particular computing device, and thelike.

A data processing system suitable for storing and/or executing programcode can be provided hereunder and can include at least one processorcommunicatively coupled, directly or indirectly, to memory elementsthrough a system bus. The memory elements can include, but are notlimited to, local memory employed during actual execution of the programcode, bulk storage, and cache memories that provide temporary storage ofat least some program code in order to reduce the number of times codemust be retrieved from bulk storage during execution. Input/outputand/or other external devices (including, but not limited to, keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening device controllers.

Network adapters also may be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems,remote printers, storage devices, and/or the like, through anycombination of intervening private or public networks. Illustrativenetwork adapters include, but are not limited to, modems, cable modems,and Ethernet cards.

The foregoing description of various aspects of the invention has beenpresented for purposes of illustration and description. It is notintended to be exhaustive or to limit the invention to the precise formdisclosed and, obviously, many modifications and variations arepossible. Such modifications and variations that may be apparent to aperson skilled in the art are intended to be included within the scopeof the invention as defined by the accompanying claims.

What is claimed is:
 1. A method for energy efficient allocation ofstorage resources in a networked computing environment, comprising:determining energy consumption characteristics of a storage workload inthe networked computing environment; selecting a type of storage devicefor handling the storage workload; developing an allocation plan toresult in a most efficient energy consumption for handling the workload,the allocation plan being based upon the energy consumptioncharacteristics, a set of device models for a set of storage deviceshaving the type, and an energy efficiency algorithm, the energyefficiency algorithm comprising applying a set of rules comprising:assigning critical data having high performance and reliabilityrequirements to a serial attached small computer system interface disk;assigning workloads with high random input/output to an solid statedrive; and assigning redundant data to a serial AT attachment; deployingthe allocation plan; and selecting at least one storage device from theset of storage devices for handling the storage workload according tothe deployed allocation plan.
 2. The method of claim 1, the networkedcomputing environment comprising a cloud computing environment.
 3. Themethod of claim 2, the at least one storage device comprising a networkshared disk (NSD) within a general parallel file system (GFPS).
 4. Themethod of claim 2, the at least one storage device comprising aplurality of NSDs associated with a plurality of redundant arrays ofindependent disks (RAIDs).
 5. The method of claim 1, the energyefficiency algorithm being based on a capacity of a set of storagedevices having the type, a load on each of the set of storage devices,and an energy consumption of the set of storage devices as derived fromthe set of device models.
 6. The method of claim 1, further comprisinghandling the storage workload according to the allocation plan.
 7. Themethod of claim 1, the energy consumption characteristics indicating alevel of energy that will be consumed for handling the storage workload,and the allocation plan allocating the storage workload so that thelevel of energy can be provided at the lowest cost.
 8. A system forenergy efficient allocation of storage resources in a networkedcomputing environment, comprising: a bus; a processor coupled to thebus; and a memory medium coupled to the bus, the memory mediumcomprising instructions to: determine energy consumption characteristicsof a storage workload in the networked computing environment; select atype of storage device for handling the storage workload; develop anallocation plan to result in a most efficient energy consumption forhandling the workload, the allocation plan being based upon the energyconsumption characteristics, a set of device models for a set of storagedevices having the type, and an energy efficiency algorithm, the energyefficiency algorithm comprising applying a set of rules comprising:assigning critical data having high performance and reliabilityrequirements to a serial attached small computer system interface disk;assigning workloads with high random input/output to an solid statedrive; and assigning redundant data to a serial AT attachment; deploythe allocation plan; and select at least one storage device from the setof storage devices for handling the storage workload according to thedeployed allocation plan.
 9. The system of claim 8, the networkedcomputing environment comprising a cloud computing environment.
 10. Thesystem of claim 9, the at least one storage device comprising a networkshared disk (NSD) within a general parallel file system (GFPS).
 11. Thesystem of claim 9, the at least one storage device comprising aplurality of NSDs associated with a plurality of redundant arrays ofindependent disks (RAIDs).
 12. The system of claim 8, the energyefficiency algorithm being based on a capacity of a set of storagedevices having the type, a load on each of the set of storage devices,and an energy consumption of the set of storage devices as derived fromthe set of device models.
 13. The system of claim 8, the memory mediumfurther comprising instructions to handle the storage workload accordingto the allocation plan.
 14. The system of claim 8, the energyconsumption characteristics indicating a level of energy that will beconsumed for handling the storage workload, and the allocation planallocating the storage workload so that the level of energy can beprovided at the lowest cost.
 15. A computer program product for energyefficient allocation of storage resources in a networked computingenvironment, the computer program product comprising a computer readablehardware storage device, and program instructions stored on the computerreadable hardware storage device, to: determine energy consumptioncharacteristics of a storage workload in the networked computingenvironment; select a type of storage device for handling the storageworkload; develop an allocation plan to result in a most efficientenergy consumption for handling the workload, the allocation plan beingbased upon the energy consumption characteristics, a set of devicemodels for a set of storage devices having the type, and an energyefficiency algorithm, the energy efficiency algorithm comprisingapplying a set of rules comprising: assigning critical data having highperformance and reliability requirements to a serial attached smallcomputer system interface disk; assigning workloads with high randominput/output to an solid state drive; and assigning redundant data to aserial AT attachment; deploy the allocation plan; and select at leastone storage device from the set of storage devices for handling thestorage workload according to the deployed allocation plan.
 16. Thecomputer program product of claim 15, the networked computingenvironment comprising a cloud computing environment.
 17. The computerprogram product of claim 16, the at least one storage device comprisinga network shared disk (NSD) within a general parallel file system(GFPS).
 18. The computer program product of claim 16, the at least onestorage device comprising a plurality of NSDs associated with aplurality of redundant arrays of independent disks (RAIDs).
 19. Thecomputer program product of claim 15, the energy efficiency algorithmbeing based on a capacity of a set of storage devices having the type, aload on each of the set of storage devices, and an energy consumption ofthe set of storage devices as derived from the set of device models. 20.The computer program product of claim 15, further comprising programinstructions stored on the computer readable hardware storage device tohandle the storage workload according to the allocation plan.
 21. Thecomputer program product of claim 15, the energy consumptioncharacteristics indicating a level of energy that will be consumed forhandling the storage workload, and the allocation plan allocating thestorage workload so that the level of energy can be provided at thelowest cost.
 22. A method for deploying a system for energy efficientallocation of storage resources in a networked computing environment,comprising: deploying a computer infrastructure being operable to:determine energy consumption characteristics of a storage workload inthe networked computing environment; select a type of storage device forhandling the storage workload; develop an allocation plan to result in amost efficient energy consumption for handling the workload, theallocation plan being based upon the energy consumption characteristics,a set of device models for a set of storage devices having the type, andan energy efficiency algorithm, the energy efficiency algorithmcomprising applying a set of rules comprising: assigning critical datahaving high performance and reliability requirements to a serialattached small computer system interface disk; assigning workloads withhigh random input/output to an solid state drive; and assigningredundant data to a serial AT attachment; deploy the allocation plan;and select at least one storage device from the set of storage devicesfor handling the storage workload according to the deployed allocationplan.